{"title":"评论小李b孟的《方差翻倍，贝叶斯变脏，河豚变大，画小孩图","authors":"T. Junk","doi":"10.51387/22-nejsds6b","DOIUrl":null,"url":null,"abstract":"This contribution is a series of comments on Prof. Xiao-Li Meng’s article, “Double Your Variance, Dirtify Your Bayes, Devour Your Pufferfish, and Draw Your Kidstogram”. Prof. Meng’s article offers some radical proposals and not-so-radical proposals to improve the quality of statistical inference used in the sciences and also to extend distributional thinking to early education. Discussions and alternative proposals are presented.","PeriodicalId":94360,"journal":{"name":"The New England Journal of Statistics in Data Science","volume":"1997 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comment on “Double Your Variance, Dirtify Your Bayes, Devour Your Pufferfish, and Draw Your Kidstogram,” by Xiao-Li Meng\",\"authors\":\"T. Junk\",\"doi\":\"10.51387/22-nejsds6b\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This contribution is a series of comments on Prof. Xiao-Li Meng’s article, “Double Your Variance, Dirtify Your Bayes, Devour Your Pufferfish, and Draw Your Kidstogram”. Prof. Meng’s article offers some radical proposals and not-so-radical proposals to improve the quality of statistical inference used in the sciences and also to extend distributional thinking to early education. Discussions and alternative proposals are presented.\",\"PeriodicalId\":94360,\"journal\":{\"name\":\"The New England Journal of Statistics in Data Science\",\"volume\":\"1997 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The New England Journal of Statistics in Data Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.51387/22-nejsds6b\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The New England Journal of Statistics in Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.51387/22-nejsds6b","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comment on “Double Your Variance, Dirtify Your Bayes, Devour Your Pufferfish, and Draw Your Kidstogram,” by Xiao-Li Meng
This contribution is a series of comments on Prof. Xiao-Li Meng’s article, “Double Your Variance, Dirtify Your Bayes, Devour Your Pufferfish, and Draw Your Kidstogram”. Prof. Meng’s article offers some radical proposals and not-so-radical proposals to improve the quality of statistical inference used in the sciences and also to extend distributional thinking to early education. Discussions and alternative proposals are presented.
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